250 Matching Annotations
  1. Last 7 days
    1. White, J.H., and W.R. Bauer, 1986, "Calculation of the twist and the writhe forrepresentative models of DNA," Journal of Molecular Biology 189, 329-341.

      This could be useful for examples / figures

    2. two distant segmentsofa DNA axisare brought very close together, then this proximity will contributeapproximately±1to the writhing number because in almost all viewsthis proximity will be seen as a crossing.

      I guess this makes sense because you're viewing DNA from different projections and it seems different - like knots/links can even have different numbers of crossings

    3. writhing numberisdefined as the average over all possible viewsofthe projected writhing number

      Again this is confusing...like what are "all possible views" isn't this infinite

    4. helical windingofthe backbone curve around theaxis

      twist

    5. DNA axis is seen to cross itself,

      writhe

    6. writheWrand twist Tw,which can be used to describe supercoiling (White, 1969)

      This may have the proof of twist + writhe = linking number

    7. For a relaxed circular DNA moleculeofthe monkey virus SV40,which has approximately 5,250 base pairs,Lkis about 500, and forbacteriophage Aofabout 48,510 base pairs,Lkis about 4,620

      Can give these as examples...with pictures!

    8. does not depend on theprojection or viewofthe pair

      knot invariant

    9. one adds allofthe signed numbers associated with thisprojection and divides by 2, one obtains the linking numberofthe curvesA and C,Lk(A,C)

      Ok so this is actually something we've done we won

    10. Such a viewgives a modified projectionofthe pairofcurve

      So this is like just a link projection

    11. topologyofknot theory to explain the actionofenzymes in carrying out the fundamental processofsite-specificrecombination.

      I feel like this one doesn't have to do with DNA as much but maybe you should do this instead idk...

    12. supercoiling helps incellular packaging of DNA in structures called nucleosomes, in whichDNA is wound around proteins called histones

      nucleosomes make up chromatin which makes up chromosomes

    13. cruciforms

      form from palindromic sequences; important for cruciform-binding proteins during DNA replication

    14. polyrnerases

      replicates DNA molecules after helicase unzips to actually build a new strand of DNA

    15. helicases

      The "unzipping" enzyme which breaks the hydrogen bonds holding the two strands together

    16. Supercoiling of closed DNA is ubiquitous in biological systems

      So it's not some niche or theoretical occurrence and it isn't just synthesized by scientists

    17. may, in general, assume almostany configuration in space

      so axis can be a knot!

    18. xis ofthe double helix may itself be a helix

      Maybe try to find a diagram for this...idk But do explain what a helix is

    19. commonlinear axis
    20. fundamental theorem

      excellent

    1. ormula for the sum ofthe first a + 1 terms of a geometric series with ratio p

      re-explain this

    2. Since the arithmetic function f(d) = d is multiplicative (verifythis!), we have that u(n) is multiplicative by Theorem 3.1

      Ok so I guess you should do the verify part

    Annotators

    1. disjointness of sub-graphs in G′ ensures that these paths do not cycle

      Maybe explain this more intuitively? It's just because the u nodes cannot be part of a cycle or isolated so they must be part of a simple path and only one simple path

    2. each node in the coregraph has degree either zero or two

      (these are the only two possible cases as each vertex has three hexagons surrounding it)

    3. ode has two incident edges

      can highlight this as well

    4. node is isolated in G′ and has degree zero

      Show this with like highlighting on the example board

    5. e1, e2, e3,and e4 belong in E

      by construction

    6. edge to belong in E′only if it lies between a X-face and an O-face

      so like delineating the boundaries

    7. If every tile of the Hex board is markedeither x or o

      i.e. the game has been played to completion

    8. u and v now have degree at most 1 since they had degree atmost two before we removed edge (u, v)

      so they're like leaves...

    9. all the nodes are isolated

      (so g is the union of N disjoint subgraphs)

    10. Each node can have degree at most two, so g can haveat most N edge

      Maybe explain this more carefully

  2. Apr 2024
    1. Figure 18

      Maybe have like an actual ribbon, perhaps that you've labeled to look like DNA, as a prop? Maybe many to pass out though this could take a while

    2. return to itsnatural twist rate,

      OK so this is a thing that DNA tries to do, best to explain that early perhaps

    3. Lk(R) = T w(R) + W r(R).

      This could definitely be a theorem that you state

    4. linking number of the ribbon is equal to the linkingnumber of this link

      so it's literally just the linking number because the two edges already are two components of a link...

    5. average signed crossover number over all possible projections of the ribbon inspace

      "average signed crossover number over all possible diagrams"...??? isn't there an infinite number of projections (diagrams)?

    6. Definition 2.3

      Seems like this one should be defined first but whatever

    7. duplex DNA

      another name for double-stranded DNA

    8. tangles

      Tangles kinda just seem like small subdiagrams of knots...with 4 endpoints (i.e. two strands enter the subdiagram and two strands leave, so four endpoints are attached to it in total)

    9. S = (-3, 0)and R = (1)

      wtf does this mean...maybe look this up and go through tangle notation. However this could be one of your theorems...though the paper is like 61 pages lol

    10. substrate molecule as N (S + T ) and the product molecule as N (S + R)

      More things to define and such

    11. knot Q

      Isn't this not a knot yet?

    12. substrate molecule (the molecule before the enzyme acts)

      other important definitions

    13. circular (cyclic) DNA

      so DNA molecules do not always have their ends joined

    14. several more general action

      so like a double crossing change, an R2 move, and a "separating" series of R-moves / planar isotopies

    15. projections

      Ok so "projections" means "diagrams" here...

    16. measure of howintertwined two components of a link are

      Is this accurate like...

    17. linking number

      Ok so actually linking number is the same methinks

    18. new terminol-ogy, writhe, to model this type of cyclic DNA.

      You can't come up with a new name for it like be serious

    Annotators

    1. 10access to the shared resources. In addition, it only considers a singleprocessor and ignores the overhead caused in changing the mode. In thefuture, we will focus on scheduling energy aware dependent periodictasks in multiprocessor MC real-time systems with the overhead ofchanging the mode

      Limitations: no consideration of precedence constraints, shared resources, multiprocessors, overhead caused in a mode change

    2. reduce energy consumption up to 32.45 and46.84% compared with Algorithm A in the normal mode and urgencymode, respectively

      picking the best values ig

    3. Effect of the ratio of Ci (LO)Ai (L

      oh ok so we're actually investigating this

    4. energy consumption up to 32.45% in the urgency mode

      maybe he's doing averages...

    5. herefore, EAU can reduce energy consumption up to41.63%

      still kinda weird idk

    6. can reduce energy consumption up to 21.17%

      still weird tf

    7. normalized energy consumption when ULOLO(Γ) was equal to 0.45

      This is why energy is set to 1

    8. In addition, the ratio of Ci(LO) and Ai(LO) was set to 5 for any task τi.

      Again assuming the WCET is like 5x the actual execution time

    9. 1.3

      This makes sense as you'd expect the utilization of all HI-criticality tasks using C_HI values to be higher than the utilization of all HI-criticality tasks using C_LO values

    10. ULOHI (Γ)

      utilization of all HI-criticality tasks using C_LO values

    11. Effect of UHIHI (Γ)

      Ok so we're differing little thingies

    12. Therefore, EAU can reduce energy consumption up to 46.30% inthe urgency mode compared with Algorithm A as shown in Fig. 9(b).

      Ok but this seems much more accurate so what's tea...

    13. actualexecution time of tasks

      biggest difference along with slowing down speed in urgent mode...why is using actual execution times valid in urgent mode again...?

    14. pplied the uniform distribution method to select theparameter of the task

      so randomly choosing a task period in between 1000 and 5000 inclusive

    15. slack time generated from the early completion jobs and the jobswere executed with Shigh in the urgency mode.

      Maybe EAU does this more efficiently than Algorithm A idk

    16. HI level task is executed with Smax in the urgencymode

      so no power-saving in the urgency mode

    17. rocessor could providediscrete normalized speed levels, i.e., [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,1.0]

      So the speeds we just discussed in the example before are not possible aurrrrkkayyy

    18. That is Pind = 0.1, Pmaxdynamic was normalized to 1 and θwas set to 0.2 in Eq. (1) [2]

      Ok let's go with that

    19. Pind = 0.1, Pmaxdynamic = 1 and θ = 0.2

      ok work let's go with that

    20. Case 3: EAU is feasible in a changed mode

      This seems fake lol

    21. sum utilization of tasks

      WHICH TASKS...

    22. asks are put intothe delay queue

      well right here it does say tasks so it maybe just means that...

    23. other is a delay queue that in-cludes the unreleased and completed job

      this doesn't make sense because aren't the number of unreleased jobs infinite? maybe the delay queue just includes the tasks? But it does specifically say unreleased and completed jobs so like what's tea...

    24. If the HI level job τijends and its execution time exceeds Ci(LO)/S under S, the system willswitch to the urgency mode

      so we only care if a HI task overruns its deadline, not a LO task

    25. Smax

      S max is 1 (normalized)

    26. Algorithm A computes the parameter x= 0.875, the optimal speed of LO tasks SLO = 0.86, and the optimal speedof HI tasks SHI = 0.90

      Ok so we're computing the optimal virtual deadline constant x, the optimal energy-saving processing speed for the LO tasks, and the optimal energy-saving processing speed for the HI tasks

    27. Ci(LO) ≤ Ai(HI)

      So the WCET for HI tasks in the normal (low) mode must be at most the actual execution time for tasks in the urgent (high) mode

    28. When the HI level task is not completed on time and its execution timeexceeds the worst case execution time (WCET) in the normal modeunder the processor speed S

      Seems like this is the same as AMC but only if a HI level task is exceeding its WCET under any processor speed S

    29. If ζ = LO, then the virtual deadline of τi is assigned to ti + T

      Ok so this is just implicit deadlines...

    30. within the time not exceeding Ci(LO)/S

      So if S is 2 we divide all task computation times by 2 it appears...

    31. execution of any task τi is completed under S

      S is the normalized processor speed - potentially the average processor speed?

    32. apply the utilization analysis method to prove that EAU isfeasible.

      Idk what this means like why is EAU "feasible" in general

    33. power-awar

      I guess just aware of how much energy it's using

    34. optimal speed iscomputed to reduce energy consumption in the normal mode

      optimal processor speed to reduce energy consumption?

    35. switches to the urgency mode inwhich only HI level tasks can be executed.

      this just sounds like adaptive mixed criticality idk

    36. MC system is driven by the power, cost, area andweight

      Means multiple real-time applications are packaged together to save power, cost, space, and weight

    37. focus on the actualexecution time of task

      hm idk this seems weird

    Annotators

    1. Wederived the energy model parameters from the state-of-the-art researches in this area [24], [28], [29], [37], [38]. Weassumed thatmaxdynamicP is normalized to 1 and0.1

      the blind leading the blind honey

    2. Static and dynamicpower components can be reduced by selecting lowerivalues for a task

      So DVFS can't do anything to help save power dissipated by I/O and memory operations

    3. )dynamicmaxi ind i i

      rho is processor speed methinks

    4. I

      Below a certain voltage (threshold) no current is supposed to flow between the terminals of a transistor but it does anyway and so this is "leaked" current - it comprises the majority of the static power consumed by the system which is not dependent on processing frequency / clock speed and exists even when the system is in a low-power sleep mode

    5. P

      should be the static power which runs to keep the clock running and maintain basic circuits - can only be deactivated by powering off the entire system. Should still be running when in energy-saving sleep move though I think

    6. P

      total power consumption by the system

    Annotators

    1. optimal x is 0.625 where f HIand f LO are 0.7 and 0.5 in each

      ok so we're choosing an optimal value for x rather than just picking the least such x which works

    Annotators

    1. urrently-executing job executes for more than itsLO -criticality WCET without signaling completion

      AMC pretty much idk

    2. ob is assigned a scheduling deadlineequal to t + ˆTi

      difference from EDF

    3. lies somewhere within the interval

      maybe have them do this if you do an exercise / handout - compute the middle of the two values

    4. x U LOLO (τ ) + U HIHI (τ ) ≤ 1

      HI-mode condition

    5. sufficientcondition for ensuring that EDF-VD successfully meets allHI -criticality deadlines during all HI -criticality behaviors ofτ

      HI-mode condition

    6. x ← U LOHI (τ )1 − U LOLO (τ )

      How the lower deadlines are computed...

    7. ll currently-active LO -criticality jobs are immediately dis-carded; henceforth, no LO -criticality job will receive anyexecution

      EDF-VD mostly uses AMC

    8. ta + ˆTi

      virtual deadline artificially decreases the period of HI-criticality tasks

    9. ta + Ti

      so just regular implicit deadlines

    10. Ti ≤ Ti

      Ok so maybe the constant x is always less than 1?

    11. τ

      so fuck Gamma I guess

    12. U yx (τ ) =∑τi ∈τ ∧χi =xCi(y)Ti

      x is the criticality of the tasks we are summing and y is the C value we are using

    13. all HI -criticality jobs receive enough execution between their releasetime and deadline to be able to signal completion.

      It's giving AMC...

    14. Each such job has a deadline that is Tk time unitsafter its release.

      implicit deadline bit

    15. erroneous behavior

      then the system is fr fucked the fucked up

    16. if evenone job Ji signals completion after executing for more thanci(LO) but no more than ci(HI) units of execution, we saythat the system has exhibited HI-criticality behavio

      it's giving AMC

    17. γi

      Actual execution time it appears

    18. sporadic

      the original paper used periodic tasks

    19. run-timecomplexity per scheduling decision was logarithmic in thenumber of task

      So scheduling overhead is logarithmic which is nice

    20. [8]

      Ok so maybe this is the source...

    21. Integrated Modular Avion-ics (IMA) [19] in aerospace and AUTOSAR

      Could be examples of systems in which this kinda stuff matters

    Annotators

    1. Mawhin, J. (2007). Le th ́eor`eme du point fixe de Brouwer: un si`ecle de m ́etamorphoses.Sciences ettechniques en perspective10(2): 175–220.

      An anthology of other proofs of Brouwer's Fixed Point Theorem

    1. nteresting trade-offbetween energy efficiency and system reliability

      recovering from faults also uses the system slack (laxity left over from when tasks are scheduled) so DVFS has to compete with that - tradeoff between saving power and system reliability

    2. static power, which includes the power tomaintain basic circuits and keep the clock running.

      we NEED that shit...

    3. reduces supplyvoltage for lower frequency requirement

      reduces how much energy is applied if like the system is operating at a lower frequency / less work is being done?

    4. CMOS

      complementary metal oxide semiconductor

    Annotators

    1. with υn = 0

      does this mean the last column is all 0s? or the last row is a 0?

    Annotators

  3. Mar 2024
    1. Utot denotes the total utilization of the task set

      so just U...

    2. Di ≤ Ti

      constrained-deadline periodic or sporadic task sets

    3. Qi

      The interval in which the preempted job is like "Let me continue for a little..."

  4. Mar 2023
    1. Formally, if L, L1 , and L2are languages on 

      all have to be on the same alphabet

    Annotators

  5. Feb 2023
    1. Whenever a tableau is infeasible wewill say that we are in Phase I of the Simplex Algorithm; Phase II ifPhase I/IIthe tableau is feasible

      Phase I is big nasty

    Annotators

    1. Workout 21

      Use a computer Set it up like Workout 17

    2. Workout 20

      Use a computer Set it up like Workout 17

    3. Workout 19.

      EASY I guess

    4. Workout 15

      should be somewhat short - apparently pretty easy...but we'll see!

  6. Jan 2023
    1. Prove

      do we need to prove it halts?

    2. Design

      call produce_match(hospitals, applicants, hospital-prefs, app-prefs) methinks

      can be like one line answer

    3. b

      if one of the functions was 5n^2 and the other was n^2 then they're both big theta of n^2

      if both are n^2 asymptotic order of growth (the same) indicate that - put them in groups or put an equal sign or something

    4. a

      think about what kind of matchings would be needed along the way to get to O(n^2) think about what series of tentative matchings we would need to get to n^2 running time looking for a general ~arbitrarily large~ thing in the looping

    5. counterexample

      this one is probably the right one BUT maybe not

      sin(x) and cos(x) (REMEMBER functions can't be negative so shift it up)

    6. For

      pick a tiny number methinks

    7. Stable Matching

      just flip applicants and hospitals

    8. Multiple slots

      may end up with unmatched applicants and/or hospitals - reasoning can involve agents not being matched at all

    1. Workout 22

      don't do this one yet because it's tooooooooo fucking hard

      have to remember the rank-null theorem or something

    2. Workout 17

      have to compute things - try it; do it by hand or by Python code

      looking for a counterexample, as vspan(X)2 is apparently not convex

    3. Workout 16

      little bit longer but not very different than workout 14

    4. Geometric Introduction: Week 2

      more things should work out this time - try like half of these for next week

    1. Hipster Coffee Tours

      how much should we say about how it works?

      looking for more of a high-level description rather than a full proof

    2. My algorithm is Ω(1)

      question the amount of insight a statement gives

    3. Banana’s algorithm is Ω(n3), so you should be careful.

      doesn't give any upper bound

    1. prove the statement by induction

      !!

    2. The number of pairs of pointsis (n2) = n(n−1)2 , and since this quantity is bounded by 12 n2

      Good to know

    3. For a woman w, we need to decide if w is currently engaged, and if sheis, we need to identify her current partner

      array again

    4. We need, for a man m, to be able to identify the highest-ranked womanto whom he has not yet proposed

      array

    5. We need to be able to identify a free man.

      linked list / stack

    6. we will only need to use twoof the simplest data structures: lists and arrays

      thank God

    7. suppose, by way of contradiction

      It appears we use a lot of proofs by contradiction in this course

    8. favoringmen

      oh hell nah...

    9. accepting those that increase the rank of her partner

      only getting better offers

    Annotators

    1. Inequality 1.4.7

      literally the answer to one of the workouts...work

    2. Workout 1.2.6

      Include picture of graph in solution

    Annotators

    1. Tier A Standards

      OK at 14:10 in the Zoom meeting on 1/19 Dr. Ramanujan said if you don't get it the third time we could STILL have a talk and figure something out...

  7. Nov 2021
    1. depicted Ellis Act evictionsthrough a series of “explosions” in which red dots eruptacross the city, corresponding to the number of unitsevicted (as filed with the San Francisco Rent Board).The map provided a quantitative yet visceral geo-graphic representation of displacement in the city, thered eviction dots leaving the city pockmarked andblemished by the end of the time lapse.

      Should've done this one LOL

    2. The Anti-Eviction Mapping Project: CounterMapping and Oral History toward Bay AreaHousing Justice

      The Anti-Eviction Mapping Project: Counter Mapping and Oral History toward Bay Area Housing Justice

  8. Oct 2021
    1. mongthespectralcolors,NorthAmericanadultsseemtopreferblueandredtogreenandviolet,andgreenandviolettoorangeandyellow.Leas

      Tea

    2. oolchildrenlikehighlysaturatedcolors,suchasbrightred,green,andblu

      Me

    3. useofspectralhuestoportrayintensitydifferencesisastrongcluethatthemapmakereitherknowslittleaboutmapdesignorcareslittleaboutthemapuser

      Yes drag them

    4. espreadignoranceofhowcolorcanhelporhurtamap.Personsunawareoftheappropriateuseofcolorincartographyareeasilyimpressedandmightacceptasusefulapoormapthatmerelylookspretty.

      Lol ME

    5. xtremelyhighorextremelylowvaluesisolat

      SPIDERS GEORG

    6. Areareallyaggregateddatabad?Sur

      What

    Annotators

    1. In the course of m y research, 1 ha ve thought of literary maps as good tools to analyze plot, but not much else, and certainly not style

      Q2 / Q3

    2. Readers needed a symbolic form capable of making sense of the nation-state

      Like a mold to conceptualize and grasp hold of with their brain tendrils...to incorporate into their understanding of the world...like gender almost (Q4)

    3. reiterated Ínter-play defines the nation as the su m of all its possible stories

      Q4

    4. Are there, in other words, events that · tend to happen in real spaces- and others that 'prefer' fictional ones? It is early to give a definitive answer, but Austen's novels certainly suggest that fictional spaces are particu-larly suited to happy endings, and the wish-fulfillment they usual! y embody. By contrast, the more pessimistic a narrative structure becomes, the more infrequent are its imaginary spaces.

      Damn that's kind of sad lol

    5. an asymmetry of the real and the i.maginary- of geography, and litera-ture- that will recur throughout the present research

      Q4 - interesting

    6. strange, harsh novelty of the modern state-and turn it into a large, exquisite home.

      Maybe

    7. Austen's novelistic geography shows all its intelli-gence.

      Yeah but didn't you just say they weren't representative of the UK??? Tf

    8. potential' .states, 1 would say, rather than actual ones.

      Ok are you seriously saying the novel singlehandedly created the nation-state...let's not get ahead of ourselves now

    9. nation-state ... found the novel. And viceversa: the novel found the nation-state. And being the only symbolic form that could represent it, it became an essential component of our modern culture.

      Q4

    10. between the novel and the geo-political reality of the nation-state

      Q4

    11. In the hope that the visual construct will be more than the su m of its parts: that it will show a shape, a pat-tern that may add something to the information that went into makingit.

      We'll be the judge of that

    12. adjective that I had never dreamt of applying to myse

      Damn sad

    13. ilaps play in them a wholly peripheral role. Decorative.

      Booooo (Q5 perhaps)

    14. points of departure, then: for m y reflections

      Q4 / Q5

    15. ortgebunden

      Help

    Annotators

    1. ghost

      wat

    2. Probably the best place to read tacos on campus

      So quirky!!

    3. The fi replace isn’t real but if you close your eyes really tight and think about Bambi fl eeing a forest fi re you can almost smell smoke

      what

    4. reehouse of Sighs.

      what

    5. Offi ce of Incandescent Light and Industrial Runoff.

      LOLLLLLLL

    6. still feel a thrill when my location on Earth is validated by satellites in space.

      Do you really? Be honest

    7. My GPS told me my worth. It told me my place in the universe.

      Bitch you have some shit to work through

    8. military intentionally reduced the system’s accuracy for everyone else under a program called Selective Availability.

      interesting...this is kind of like a Monmonier Chapter 8 moment

    9. Spiritual validation: the satellite gods in the sky have deemed me to be exactly where I think I am.

      I don't relate to this but work

    10. The fi gure on the screen roughly corresponded to my independent estimate,” Jack says with wonder. An “estimate,” he continues, “feebly arrived at after long searches through documents, tormented arithmetic. Waves of relief and gratitude fl owed over me. The system had blessed my life. I felt its support and approval.

      what

    11. I don’t want to know to where I’m going or how to get there. I want to know where I am.

      So fucking lame omg

    12. I am strangely drawn to the power of GPS, to the possibility of knowing my precise location on the face of the planet.

      Deeply weirdo shit

    13. These counter-sites are juxtaposed against one another, often telling very different stories about the same location

      Could you PLEASE just give a fucking example like is it that hard

    14. Michel Foucault,

      Here we go

    15. A game in which teams work to leave fragments of a narrative around various spaces using existing locative social media.

      My favorite game for sure

    16. Mark Sample

      I know this bitch!!!

    Annotators

    1. CIA pocket atlas used by American foreign service personnel has a detailed, fully indexed, eas

      lol good try

    Annotators